ScalLoc: Continuous Oblique Thermal UAV Geo-Localization with a 2D Satellite Map

Xiaoran Zhang; Yu Liu; Jinyu Liang; Huaxin Xiao

cs.CVOct 10, 2026

ScalLoc: Continuous Oblique Thermal UAV Geo-Localization with a 2D Satellite Map

Authors: Xiaoran Zhang, Yu Liu, Jinyu Liang, Huaxin Xiao

Organizations: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China

Abstract

Cross-modal Thermal Geo-localization (TG) provides an illumination-robust positioning solution for Unmanned Aerial Vehicles (UAVs) in Global Navigation Satellite System (GNSS)-denied environments. However, existing thermal geo-localization methods mainly focus on near-nadir observations or depend on map-side elevation priors, limiting their applicability to continuous oblique thermal localization with only geo-referenced 2D satellite maps. To address this problem, we propose ScalLoc, a scale-aligned local-to-map localization framework for continuous oblique thermal UAV geo-localization. ScalLoc follows a reconstruction--refinement--anchoring--propagation design: it reconstructs local window geometry from thermal sequences, refines camera poses using track-based pose optimization, adaptively anchors each window to the satellite map, and propagates validated states to recover frame-level global trajectories. To support systematic evaluation, we construct Thermal-Oblique, a real thermal UAV sequence dataset containing 216 urban and rural day--night trajectories and 28,112 thermal frames with frame-level annotations. Extensive experiments demonstrate that ScalLoc reduces the mean localization error from 112.69 m to 26.97 m compared with the VGGT-only configuration, corresponding to a 76.07% reduction, and achieves the lowest frame-weighted mean position error among the compared methods. These results demonstrate the feasibility of continuous oblique thermal geo-localization using only lightweight 2D satellite maps, without requiring digital surface models or preconstructed 3D maps. Code and dataset are available at https://github.com/FloralHercules/ScalLoc.

Figures & tables

Fig. 1: Comparison of UAV geo-localization frameworks with different reference-map priors. (a) Existing approaches using map-side elevation or 3D priors; (b) ScalLoc using only a geo-referenced 2D satellite map.

Dataset	Year	UAV Modality	Reference Map	Scale (images)	Viewpoint	Metric Position	Trajectory-level Eval.
University-1652 [ 11 ]	2020	Synthetic RGB	Satellite	50.2K	Multi-view	$\times$	$\times$
SUES-200 [ 15 ]	2023	Real RGB	Satellite	24.1K	Multi-view	$\times$	$\times$
UAVD4L [ 8 ]	2024	Real RGB	3D mesh + DSM	1.6K	Near-nadir	✓	$\times$
UAVD4L-LoD [ 9 ]	2024	Real RGB	LoD3.0 model	3.8K	Near-nadir	✓	$\times$
AnyVisLoc [ 16 ]	2025	Real RGB	Satellite + DSM	18.0K	Multi-view	✓	$\times$
OrthoLoC [ 10 ]	2025	Real RGB	Satellite + DSM	16.4K	Nadir + oblique	✓	$\times$

TABLE I: Comparison of representative UAV geo-localization datasets. “Metric Position” indicates support for metric position evaluation, while “Trajectory-level Eval.” indicates support for continuous trajectory-level localization with frame-level annotations.

Fig. 2: Overview of ScalLoc. The framework performs VGGT-based window reconstruction, track-based pose refinement, motion-adaptive satellite anchoring, anchor validation, and window-to-window propagation to recover frame-level UTM positions and headings from oblique thermal sequences.

Fig. 3: Overview of the Thermal-Oblique dataset. (a) Spatial distribution of 216 trajectories. (b)–(d) Representative straight, curve, and orbit trajectories. (e) Example thermal sequences from different scene categories.

Fig. 4: Qualitative operation examples of ScalLoc. (a) Orbit motion uses BEV-to-satellite matching by converting local VGGT reconstructions into BEV representations. (b) Forward motion uses direct oblique image-to-satellite matching with selected thermal frames and satellite tiles. Both branches propagate validated anchor states to recover continuous trajectories.

Fig. 5: Globally anchored point-cloud merging in a shared coordinate frame. Rows show (a) straight, (b) curve, and (c) orbit trajectories, while columns show progressive window merging. Different colors denote different windows.

Method	Straight		Curve		Orbit		All Frames ME $\pm$ SD	All Frames MedE	Time Per Frame
Method	Pos. Acc@5/10/20	Yaw Acc@3/5/10	Pos. Acc@5/10/20	Yaw Acc@3/5/10	Pos. Acc@5/10/20	Yaw Acc@3/5/10	All Frames ME $\pm$ SD	All Frames MedE	Time Per Frame
A. DSM-assisted Geo-localization Methods
SCC-Loc [ 18 ]	16.53 / 31.50 / 43.70	28.55 / 44.27 / 55.47	19.23 / 36.86 / 51.08	29.03 / 49.30 / 63.46	27.66 / 51.06 / 65.64	32.37 / 58.17 / 74.87	$566.97\pm 1317.47$	15.43	1.97
OrthoLoC [ 10 ]	23.16 / 40.15 / 49.35	29.40 / 47.26 / 57.32	26.19 / 44.50 / 55.37	27.94 / 49.35 / 62.97	34.91 / 60.85 / 74.86	37.25 / 64.60 / 81.87	$522.81\pm 1237.20$	10.36	1.36
OrthoTrack [ 17 ]	31.12 / 46.92 / 55.62	45.32 / 67.85 / 87.18	21.47 / 33.15 / 39.00	24.08 / 38.27 / 50.38	21.13 / 41.92 / 58.33	34.16 / 55.43 / 75.09	$234.28\pm 367.51$	17.85	0.55
B. Scale-aligned Sequence-only Methods

TABLE II: Quantitative comparison under different trajectory patterns on Thermal-Oblique. The best and second-best results are highlighted in bold and underlined , respectively.

Fig. 6: Trajectory-wise position and yaw errors on Thermal-Oblique. Dashed lines denote frame-weighted mean errors.

Fig. 7: Qualitative trajectory comparison on Thermal-Oblique. (a) Straight, (b) curve, and (c) orbit trajectories. The reference trajectory is shown in green, ScalLoc in blue, OrthoTrack in pink, and VGGT-SLAM in black. ScalLoc stays close to the reference trajectory across different motion patterns.

Method	Pos. Acc@5/10/20	Yaw Acc@3/5/10	ME $\pm$ SD	MedE
VGGT	2.78 / 5.58 / 12.46	53.14 / 64.24 / 77.50	$112.69\pm 110.09$	78.98
VGGT + BA	4.70 / 8.25 / 16.28	56.34 / 67.57 / 80.31	$103.82\pm 107.68$	72.36
VGGT + Anchor	12.74 / 31.26 / 61.21	42.51 / 71.02 / 88.96	$36.15\pm 82.08$	15.54
VGGT + BA + Anchor (Without Validation)	3.81 / 15.46 / 30.18	60.32 / 70.32 / 83.25	$76.97\pm 213.67$	45.66
VGGT + BA + Anchor (Ours)	17.81 / 39.46 / 69.18	45.46 / 74.68 / 92.25	$\mathbf{26.97\pm 71.67}$	12.66

TABLE III: Ablation results of VGGT reconstruction, track-based pose refinement, and satellite anchoring on Thermal-Oblique.

Trajectory	VGGT Time	BA Time	Anchor Time	Window Time	Avg. Frame Time
Straight	2.87	0.08	2.88	6.73	0.57
Curve	2.65	0.06	2.79	6.32	0.56
Orbit	2.66	0.06	1.74	5.22	0.44

TABLE IV: Runtime breakdown of ScalLoc on representative trajectories. All times are reported in seconds.

Fig. 8: Performance of ScalLoc across flight heights and camera pitch angles. The first row shows ME in meters, and the second row shows Pos. Acc@20 for straight, curve, and orbit trajectories.

Scene	Lighting	Pos. Acc@5/10/20	Yaw Acc@3/5/10	ME $\pm$ SD	MedE
Urban	Day	24.54 / 49.77 / 76.35	43.11 / 78.15 / 93.44	$24.31\pm 65.41$	10.07
Urban	Night	19.11 / 44.05 / 74.32	55.23 / 81.36 / 94.84	$23.01\pm 49.90$	11.26
Rural	Day	12.33 / 29.56 / 63.02	42.09 / 72.08 / 92.61	$19.98\pm 17.87$	15.59
Rural	Night	15.57 / 35.06 / 63.58	41.88 / 67.67 / 88.33	$40.03\pm 113.55$	14.71

TABLE V: Performance across scene and lighting conditions.

Window Size	Keyframe Count	Straight		Curve		Orbit		All Frames		Time Per Frame
Window Size	Keyframe Count	ME	MedE	ME	MedE	ME	MedE	ME	MedE	Time Per Frame
8	1	19.77	6.93	18.09	9.82	59.21	26.77	33.78	12.24	0.614
	3	16.84	8.30	13.85	11.41	59.21	26.77	33.72	13.26	0.740
	5	15.14	8.11	13.38	11.35	59.21	26.77	35.20	13.10	0.868
12	1	17.21	6.90	18.99	10.97	48.41	18.60	29.10	11.76	0.507
	3	13.99	8.69	14.90	12.53	48.41	18.60	26.97	12.66	0.561

TABLE VI: Parameter analysis of window size and keyframe count. The best results are highlighted in bold .

Pitch Perturbation	Relative Height Perturbation	Pos. Acc@20	Yaw Acc@10	ME $\pm$ SD	MedE
$0.00^{\circ}$	$0.00\%$	69.18	92.25	$26.97\pm 71.67$	12.66
	$\pm 10.00\%$	68.71	92.24	$26.87\pm 71.44$	12.80
	$\pm 20.00\%$	68.29	92.30	$27.34\pm 71.65$	12.96
$\pm 5.00^{\circ}$	$0.00\%$	67.42	92.48	$27.78\pm 71.91$	13.23
	$\pm 10.00\%$	67.27	92.36	$28.23\pm 72.58$	13.40
	$\pm 20.00\%$	66.37	92.24	$28.75\pm 72.64$	13.60

TABLE VII: Robustness analysis under different pitch and relative-height perturbations.

Cross-View Geo-Localization

Cross-view geo-localization matches ground, drone, or aerial images to maps or satellite views to estimate where they were captured.

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SCC-Loc: A Unified Semantic Cascade Consensus Framework for UAV Thermal Geo-Localization

Xiaoran Zhang, Yu Liu, Jinyu Liang, Kangqiushi Li+2

College of Systems Engineering, National University of Defense Technology, Changsha 410073, China

Cross-View Geo-Localization GNSS-Denied Localization Cross-Modal Image Registration UAV Remote Sensing

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Cross-modal Thermal Geo-localization (TG) provides a robust, all-weather solution for Unmanned Aerial Vehicles (UAVs) in Global Navigation Satellite System (GNSS)-denied environments. However, profound thermal-visible modality gaps introduce severe feature ambiguity, systematically corrupting conventional coarse-to-fine registration. To dismantle this bottleneck, we propose SCC-Loc, a unified Semantic-Cascade-Consensus localization framework. By sharing a single DINOv2 backbone across global retrieval and MINIMA

_{\text{RoMa}}

matching, it minimizes memory footprint and achieves zero-shot, highly accurate absolute position estimation. Specifically, we tackle modality ambiguity by introducing three cohesive components. First, we design the Semantic-Guided Viewport Alignment (SGVA) module to adaptively optimize satellite crop regions, effectively correcting initial spatial deviations. Second, we develop the Cascaded Spatial-Adaptive Texture-Structure Filtering (C-SATSF) mechanism to explicitly enforce geometric consistency, thereby eradicating dense cross-modal outliers. Finally, we propose the Consensus-Driven Reliability-Aware Position Selection (CD-RAPS) strategy to derive the optimal solution through a synergy of physically constrained pose optimization. To address data scarcity, we construct Thermal-UAV, a comprehensive dataset providing 11,890 diverse thermal queries referenced against a large-scale satellite ortho-photo and corresponding spatially aligned Digital Surface Model (DSM). Extensive experiments demonstrate that SCC-Loc establishes a new state-of-the-art, suppressing the mean localization error to 9.37 m and providing a 7.6-fold accuracy improvement within a strict 5-m threshold over the strongest baseline. Code and dataset are available at https://github.com/FloralHercules/SCC-Loc.

Abstract

Cross-View Geo-Localization

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